Link at https://docs.google.com/document/d/1bdNeOMAYY90k8FAbPRWGBgS1YBEdP09kP0vcW0tNgPc/edit?usp=sharing
european <- read_csv("01-cleaning_data_data/european_recoded.csv")
australian <- read_csv("01-cleaning_data_data/australian_recoded.csv")
dim(european)
[1] 209 115
dim(australian)
[1] 269 146
european$EU <- 1
australian$AU <- 1
all <- merge(european,australian,all = TRUE)
table(all$EU,all$AU,useNA = "always")
1 <NA>
1 0 209
<NA> 269 0
demographics_var <- c("Age","Gender","L1","speak.other.L2","study.other.L2","origins","year.studyL2","other5.other.ways","degree","roleL2.degree","study.year","prof","L2.VCE","uni1.year","Context")
l2School <- "\\.L2school$"
l2School_variables <- colnames(all)[grep(l2School,colnames(all))]
#table(all$L1,all$Context) # too many levels - needs to be cleaned (ex tot number of languages?)
table(all$L1,useNA = "always")
Afrikaans Albanian Burmese Cantonese Chinese
1 2 1 4 7
Croatian Dutch English English and Dutch German
1 1 201 2 77
German and English German and Turkish I Indonesian Italian
2 1 1 1 89
Japanese Mandarin Persian Persian (Farsi) Romanian
1 5 1 1 3
Russian Sindhi Slovak Spanish Turkish
2 1 1 3 1
Ukrainian <NA>
1 67
ggplot(all,aes(x=L1,fill=Context)) + geom_bar() + coord_flip() + ggtitle("First Language") + labs(y="N. of participants",x="")+theme_bw()
#table(all$speak.other.L2,all$Context)
L2 <- data.frame(Freq=table(all$speak.other.L2)[order(table(all$speak.other.L2),decreasing = TRUE)],
L2=names(table(all$speak.other.L2))[order(table(all$speak.other.L2),decreasing = TRUE)]) # too many levels - needs to be cleaned (ex tot number of languages?)
head(L2)
table(all$origins,useNA = "always")
No Yes <NA>
323 89 66
table(all$year.studyL2)
0 years 1- 3 years 1-3 years
69 12 11
4-6 years First year of primary school Kindergarten
61 80 30
Less than a year more than 6 years Other
27 49 72
table(all$degree)
BA in Anglistik BA in Nordamerikastudien
43 4
HUM HUM.SCI
129 6
LA Lingue e letterature straniere
36 82
Lingue, mercati e culture dell'Asia QC
13 5
SCI
86
all$study.year[is.na(all$study.year)] <- all$uni1.year[is.na(all$study.year)]
#table(all$study.year)
all$study.year <- ifelse(all$study.year == "Already graduated after 5 semesters in March 2016, was interested in survery/study, sorry.","6th semester",all$study.year)
table(all$study.year)
1st semester 1st year 2nd semester 2nd year 3rd semester
72 255 5 37 5
3rd year 3rd year of Master 4th year bachelor 5th semester 6th semester
20 1 8 2 3
7th year Master
1 3
table(all$prof,useNA = "always")
Advanced Elementary Intermediate Upper-intermediate <NA>
78 105 84 146 65
all$study.year[is.na(all$study.year)] <- all$uni1.year[is.na(all$study.year)]
# Filter only subject that we want to include in the study
# names(table(all$study.year))[1] = 1st semester"
filtered <- subset(all, (study.year == "1st year") | (study.year == names(table(all$study.year))[1]) & year.studyL2 != "0 years")
table(filtered$Context)
English in Germany English in Italy German in Australia Italian in Australia
71 91 89 75
table(all$Context)
English in Germany English in Italy German in Australia Italian in Australia
96 113 146 123
all <- filtered
demographics_var <- c("Age","Gender","L1","speak.other.L2","study.other.L2","origins","year.studyL2","other5.other.ways","degree","roleL2.degree","study.year","prof","L2.VCE","uni1.year","Context")
l2School <- "\\.L2school$"
l2School_variables <- colnames(all)[grep(l2School,colnames(all))]
ggplot(all,aes(x=L1,fill=Context)) + geom_bar() + coord_flip() + ggtitle("First Language") + labs(y="N. of participants",x="") + theme_bw()
table(all$L1,all$Context)
English in Germany English in Italy German in Australia Italian in Australia
Afrikaans 0 0 1 0
Albanian 0 1 0 0
Cantonese 0 0 2 0
Chinese 0 2 2 0
Dutch 1 0 0 0
English 1 0 75 73
English and Dutch 0 0 2 0
German 64 0 0 0
German and English 1 0 1 0
I 0 0 0 1
Indonesian 0 0 1 0
Italian 0 87 0 0
Japanese 0 0 1 0
Mandarin 0 0 1 1
Persian (Farsi) 0 0 1 0
Romanian 0 0 1 0
Russian 2 0 0 0
Sindhi 0 0 1 0
Spanish 1 0 0 0
Turkish 1 0 0 0
Ukrainian 0 1 0 0
table(all$degree,all$L1)
Afrikaans Albanian Cantonese Chinese Dutch English
BA in Anglistik 0 0 0 0 0 1
BA in Nordamerikastudien 0 0 0 0 0 0
HUM 1 0 2 0 0 92
HUM.SCI 0 0 0 0 0 5
LA 0 0 0 0 1 0
Lingue e letterature straniere 0 1 0 1 0 0
Lingue, mercati e culture dell'Asia 0 0 0 1 0 0
QC 0 0 0 0 0 4
SCI 0 0 0 2 0 45
English and Dutch German German and English I Indonesian
BA in Anglistik 0 34 1 0 0
BA in Nordamerikastudien 0 4 0 0 0
HUM 1 0 0 1 0
HUM.SCI 0 0 0 0 0
LA 0 25 0 0 0
Lingue e letterature straniere 0 0 0 0 0
Lingue, mercati e culture dell'Asia 0 0 0 0 0
QC 0 0 0 0 0
SCI 1 0 1 0 1
Italian Japanese Mandarin Persian (Farsi) Romanian Russian
BA in Anglistik 0 0 0 0 0 2
BA in Nordamerikastudien 0 0 0 0 0 0
HUM 0 0 0 0 0 0
HUM.SCI 0 0 0 0 0 0
LA 0 0 0 0 0 0
Lingue e letterature straniere 75 0 0 0 0 0
Lingue, mercati e culture dell'Asia 12 0 0 0 0 0
QC 0 0 0 0 0 0
SCI 0 1 2 1 1 0
Sindhi Spanish Turkish Ukrainian
BA in Anglistik 0 1 0 0
BA in Nordamerikastudien 0 0 0 0
HUM 0 0 0 0
HUM.SCI 0 0 0 0
LA 0 0 1 0
Lingue e letterature straniere 0 0 0 1
Lingue, mercati e culture dell'Asia 0 0 0 0
QC 0 0 0 0
SCI 1 0 0 0
#Filter by L1
nc <- names(table(all$Context))
table(all$Context)
English in Germany English in Italy German in Australia Italian in Australia
71 91 89 75
l1_filter <- all[(all$Context == nc[1] & (all$L1 == "German" | all$L1 == "German and English")) |
(all$Context == nc[2] & (all$L1 == "Italian")) |
(all$Context == nc[3] & (all$L1 == "English" | all$L1 == "English and Dutch" | all$L1 == "German and English")) |
(all$Context == nc[4] & (all$L1 == "English" | all$L1 == "English and Dutch" | all$L1 == "German and English")),]
#all <- l1_filter
# do not filter for L1
all <- all
# subset demographics
demo <- subset(all,select=c("Resp.ID",demographics_var,l2School_variables))
# Numeri finali
table(l1_filter$Context)
English in Germany English in Italy German in Australia Italian in Australia
65 87 78 73
table(all$Context)
English in Germany English in Italy German in Australia Italian in Australia
71 91 89 75
missing_bySample <- rowSums(is.na(demo))
names(missing_bySample) <- demo$Resp.ID
missing_byVar <- colSums(is.na(demo))
names(missing_byVar) <- colnames(demo)
barplot(missing_bySample)
d <- data.frame(miss=missing_byVar)
d$varID <- rownames(d)
ggplot(data=d,aes(x=varID,y=miss)) + geom_bar(stat="identity") + theme_bw() +theme(axis.text.x = element_text(angle = 45, hjust = 1))
demo_missing <- demo %>% group_by(Context) %>% summarise(roleL2.degree_na = sum(is.na(roleL2.degree)),
L2.VCE_na = sum(is.na(L2.VCE)),
other5.other.ways_na=sum(is.na(other5.other.ways )),
uni1.year_na = sum(is.na(uni1.year)),
primary1.L2school_na=sum(is.na(primary1.L2school)),
CLS3.L2school_na = sum(is.na(CLS3.L2school)),
VSL4.L2school_na=sum(is.na(VSL4.L2school)),
degree = sum(is.na(degree)),
schooL2country5.L2school_na=sum(is.na(schooL2country5.L2school)))
# We do not filter for speak.other.L2 or study.other.L2
#demo[is.na(demo$speak.other.L2),]
# teniamo
#demo[is.na(demo$study.other.L2),]
missing_bySample[names(missing_bySample) == "5166861581"]
5166861581
10
#demo[is.na(demo$year.studyL2),]
missing_bySample[names(missing_bySample) == "5378798787"]
5378798787
3
# remove NA from degree
#table(demo$degree,useNA = "always")
# Remove people
all <- all[!is.na(all$degree),]
table(all$Context)
English in Germany English in Italy German in Australia Italian in Australia
70 91 88 74
write.csv(all,file.path("02-descriptive_data/context-merged_filtered.csv"))
# add numbers on the bar
# tabAge <- t(table(all$Age,all$Context))
# ggplot(all,aes(x=Age,fill=Context)) + geom_bar(position="dodge",colour="white") + labs(y="N participants") + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + draw_grob(tableGrob(tabAge), x=2.5, y=40, width=0.3, height=0.4) + ggtitle("Participants by age")
# tabAge
tabAge <- t(table(all$Age,all$Context))
ggdf <- data.frame(Age = rep(colnames(tabAge),each=4)[!(as.numeric(tabAge) == 0)],
N.Participants = as.numeric(tabAge)[!(as.numeric(tabAge) == 0)],
Context = rep(rownames(tabAge),times=3)[!(as.numeric(tabAge) == 0)])
ggplot(ggdf,aes(x=Age,y=N.Participants,fill=Context)) + geom_bar(position="dodge",colour="white",stat="identity") + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + ggtitle("Participants by age")+
geom_text(aes(label = N.Participants), hjust=0.5, vjust=-0.25, size = 2.5,position=position_dodge(width=0.9))
# add numbers on the bar
tabAge <- t(table(all$Gender,all$Context))
ggdf <- data.frame(Gender = rep(colnames(tabAge),each=4)[!(as.numeric(tabAge) == 0)],
N.Participants = as.numeric(tabAge)[!(as.numeric(tabAge) == 0)],
Context = rep(rownames(tabAge),times=3)[!(as.numeric(tabAge) == 0)])
ggplot(ggdf,aes(x=Gender,y=N.Participants,fill=Context)) + geom_bar(position="dodge",colour="white",stat="identity") + labs(y="N participants") + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + ggtitle("Participants by gender")+ geom_text(aes(label = N.Participants), hjust=0.5, vjust=-0.25, size = 2.5,position=position_dodge(width=0.9))
# add numbers on the bar
tabAge <- t(table(all$origins,all$Context))
ggplot(all,aes(x=origins,fill=Context)) + geom_bar(position="dodge",colour="white") + ggtitle("Origins by context") + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + draw_grob(tableGrob(tabAge), x=2, y=60, width=0.3, height=0.4) + ggtitle("Participants by origins")
tabAge
No Yes
English in Germany 65 5
English in Italy 90 1
German in Australia 63 25
Italian in Australia 36 38
tabAge <- t(table(all$prof,all$Context))
ggplot(all,aes(x=Context,fill=prof)) + geom_bar(position="dodge",colour="white") + ggtitle("Proficiency by context") + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + draw_grob(tableGrob(tabAge), x=2, y=80, width=0.3, height=0.4)
tabAge
Advanced Elementary Intermediate Upper-intermediate
English in Germany 38 0 5 27
English in Italy 23 2 9 57
German in Australia 4 32 25 27
Italian in Australia 0 29 29 16
tabAge <- t(table(all[all$Context != "English in Germany" & all$Context != "English in Italy","L2.VCE"],all[all$Context != "English in Germany" & all$Context != "English in Italy",'Context'],useNA = "always"))
tabAge <- tabAge[-3,]
ggplot(all[all$Context != "English in Germany" & all$Context != "English in Italy",],aes(x=Context,fill=L2.VCE)) + geom_bar(position="dodge",colour="white") + ggtitle("L2.VCE by context") + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + draw_grob(tableGrob(tabAge), x=2, y=80, width=0.3, height=0.4)
# year study L2
table(all$year.studyL2,all$other.year.studyL2.richi)
BILINGUAL FIRST.YEAR.SECONDARY FOURTH.YEAR.PRIMARY LOWER.SECONDARY
0 years 0 0 0 0
1- 3 years 0 0 0 0
1-3 years 0 0 0 0
4-6 years 0 0 0 0
First year of primary school 0 0 0 0
Kindergarten 0 0 0 0
Less than a year 0 0 0 0
more than 6 years 0 0 0 0
Other 4 10 5 4
PERSONAL SECOND.YEAR.PRIMARY SECOND.YEAR.SECONDARY
0 years 0 0 0
1- 3 years 0 0 0
1-3 years 0 0 0
4-6 years 0 0 0
First year of primary school 0 0 0
Kindergarten 0 0 0
Less than a year 0 0 0
more than 6 years 0 0 0
Other 2 2 2
THIRD.YEAR.PRIMARY
0 years 0
1- 3 years 0
1-3 years 0
4-6 years 0
First year of primary school 0
Kindergarten 0
Less than a year 0
more than 6 years 0
Other 28
all$year.studyL2 <- ifelse(all$year.studyL2 == "Other",all$other.year.studyL2.richi,all$year.studyL2 )
# European context
ggplot(all[all$Context == "English in Germany" | all$Context == "English in Italy",],aes(x=degree,fill=year.studyL2)) + geom_bar(position="dodge",colour="white") + theme_bw() + ggtitle("Degree by study year L2, by Context") + facet_grid(~Context,scales="free") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + labs(y = "N participants", x = "degree")
# Australian context
tabAge <- t(table(all[all$Context == "Italian in Australia" | all$Context == "German in Australia",'degree'],all[all$Context == "Italian in Australia" | all$Context == "German in Australia",'Context']))
ggplot(all[all$Context == "Italian in Australia" | all$Context == "German in Australia",],aes(x=Context,fill=degree)) + geom_bar(position="dodge",colour="white") + theme_bw() + ggtitle("Degree in Australian Contexts") + draw_grob(tableGrob(tabAge), x=1., y=40, width=0.3, height=0.4)
tabAge
HUM HUM.SCI QC SCI
German in Australia 47 3 4 34
Italian in Australia 50 2 0 22
# Australian context
tabAge <- t(table(all[all$Context == "English in Italy" | all$Context == "English in Germany",'degree'],all[all$Context == "English in Italy" | all$Context == "English in Germany",'Context']))
ggplot(all[all$Context == "English in Italy" | all$Context == "English in Germany",],aes(x=Context,fill=degree)) + geom_bar(position="dodge",colour="white") + theme_bw() + ggtitle("Degree in European Contexts")
tabAge
BA in Anglistik BA in Nordamerikastudien LA Lingue e letterature straniere
English in Germany 39 4 27 0
English in Italy 0 0 0 78
Lingue, mercati e culture dell'Asia
English in Germany 0
English in Italy 13
all_melt <- melt(all,id.vars = c("Resp.ID","Gender","Age","prof","Context","study.year"),
measure.vars = likert_variables_all)
all_melt$value <- factor(all_melt$value,levels=c("Strongly disagree","Disagree","Not sure","Agree","Strongly agree"))
all_melt <- all_melt %>% separate(variable,into=c("item","type"),sep="\\.",remove=FALSE)
Too few values at 646 locations: 9368, 9369, 9370, 9371, 9372, 9373, 9374, 9375, 9376, 9377, 9378, 9379, 9380, 9381, 9382, 9383, 9384, 9385, 9386, 9387, ...
ggplot(all_melt,aes(x=variable,fill=value)) + geom_bar(position = "stack",colour="black") +
facet_grid(Context~type,scales = "free")+theme(axis.text.x = element_text(angle = 45, hjust = 1),axis.text=element_text(size=8)) + ggtitle("Filtered dataset") + scale_fill_manual(values=c("#ca0020","#f4a582","#ffffbf","#abd9e9","#2c7bb6","grey"))
filt_sum <- all_melt %>% group_by(Context,variable,type,value) %>% dplyr::summarise(Ngroup=length(value))
ggplot(filt_sum,aes(x=value,y=Ngroup,colour=Context,group=interaction(variable, Context))) + geom_line() + geom_point() + facet_wrap(~type,scales = "free")+theme(axis.text.x = element_text(angle = 45, hjust = 1))
convertToNumber <- function(column){
column <- factor(column,levels = c("Strongly disagree","Disagree","Not sure","Agree","Strongly agree"))
column_number <- as.numeric(column)
return(column_number)
}
table(all$Context)
English in Germany English in Italy German in Australia Italian in Australia
70 91 88 74
convert_likert <- data.frame(apply(subset(all,select=likert_variables_all),2,convertToNumber))
colnames(convert_likert) <- paste0(colnames(convert_likert),"1")
likert_variables1 <- paste0(likert_variables_all,"1")
# join the converted variables to the filtered dataset
filtered_conv <- cbind(all,convert_likert)
table(filtered_conv[,likert_variables_all[4]],filtered_conv[,likert_variables1[4]],useNA = "always")
1 2 3 4 5 <NA>
Agree 0 0 0 121 0 0
Disagree 0 10 0 0 0 0
Not sure 0 0 39 0 0 0
Strongly agree 0 0 0 0 152 0
Strongly disagree 1 0 0 0 0 0
<NA> 0 0 0 0 0 0
write.csv(filtered_conv,"02-descriptive_data/merged_filtered_likertNumber.csv",row.names = FALSE)
cov <- cor(filtered_conv[filtered_conv$Context == "Italian in Australia",likert_variables1[!(likert_variables1 %in% "necessity1")]],method = "pearson",use="pairwise.complete.obs")
row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
row_infos$Variables <- as.character(row_infos$Variables)
rownames(row_infos) <- rownames(cov)
row_infos$Variables[which(is.na(row_infos$Variables))] <- c("educated")
row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]
ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
ann_colors_wide <- list(Variables=c(comm1="#bd0026",educated="#b35806", id1="#f6e8c3",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))
#pheatmap(cov, main = "Italian in Australia",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE], annotation_colors = ann_colors_wide,breaks=seq(-1,1,0.2),col=c("#67001f","#b2182b","#d6604d","#f4a582","#fddbc7","#f7f7f7","#d1e5f0","#92c5de","#4393c3","#2166ac","#053061"),show_colnames = FALSE,width = 7,height = 7)
###################
diag(cov) <- NA
pheatmap(cov, main = "Italian in Australia",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
, annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))
cov <- cor(filtered_conv[filtered_conv$Context == "German in Australia",likert_variables1[!(likert_variables1 %in% "necessity1")]],method = "pearson",use="pairwise.complete.obs")
row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
row_infos$Variables <- as.character(row_infos$Variables)
rownames(row_infos) <- rownames(cov)
row_infos$Variables[which(is.na(row_infos$Variables))] <- c("educated")
row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]
ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
ann_colors_wide <- list(Variables=c(comm1="#bd0026",educated="#b35806", id1="#f6e8c3",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))
diag(cov) <- NA
pheatmap(cov, main = "German in Australia",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
, annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))
cov <- cor(filtered_conv[filtered_conv$Context == "English in Germany",likert_variables1[!(likert_variables1 %in% c("reconnect.comm1", "speakersmelb.comm1","comecloser.comm1","educated1"))]],method = "pearson",use="pairwise.complete.obs")
row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
row_infos$Variables <- as.character(row_infos$Variables)
rownames(row_infos) <- rownames(cov)
row_infos$Variables[which(is.na(row_infos$Variables))] <- c("necessity")
row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]
ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
ann_colors_wide <- list(Variables=c(id1="#f6e8c3",necessity="#b35806",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))
diag(cov) <- NA
pheatmap(cov, main = "English in Germany",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
, annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))
cov <- cor(filtered_conv[filtered_conv$Context == "English in Italy",likert_variables1[!(likert_variables1 %in% c("reconnect.comm1","speakersmelb.comm1","comecloser.comm1","educated1"))]],method = "pearson",use="pairwise.complete.obs")
row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
row_infos$Variables <- as.character(row_infos$Variables)
rownames(row_infos) <- rownames(cov)
row_infos$Variables[which(is.na(row_infos$Variables))] <- "necessity"
row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]
ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
ann_colors_wide <- list(Variables=c(comm1="#bd0026",necessity="#b35806", id1="#f6e8c3",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))
diag(cov) <- NA
pheatmap(cov, main = "English in Italy",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
, annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))
cov <- cor(filtered_conv[,likert_variables1],method = "pearson",use="pairwise.complete.obs")
row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
row_infos$Variables <- as.character(row_infos$Variables)
rownames(row_infos) <- rownames(cov)
row_infos$Variables[which(is.na(row_infos$Variables))] <- c("necessity","educated")
row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]
ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
ann_colors_wide <- list(Variables=c(comm1="#bd0026",educated="orange", id1="#f6e8c3",instru1="#35978f",necessity="#b35806",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))
diag(cov) <- NA
pheatmap(cov, main = "All Contexts",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
, annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))
sets <- list(id.var=likert_variables1[grep("\\.id1$",likert_variables1)],
ought.var=likert_variables1[grep("\\.ought1$",likert_variables1)],
intr.var=likert_variables1[grep("\\.intr1$",likert_variables1)],
instru.var=likert_variables1[grep("\\.instru1$",likert_variables1)],
integr1.var=likert_variables1[grep("\\.integr1$",likert_variables1)],
prof.var=likert_variables1[grep("\\.prof1$",likert_variables1)],
post.var=likert_variables1[grep("\\.post1$",likert_variables1)],
comm.var=likert_variables1[grep("\\.comm1$",likert_variables1)])
get_alpha <- function(dataMot,
var=sets$id.var){
var_alpha <- alpha(dataMot[,var])
dataf <- data.frame(alpha=var_alpha$total,
drop = var_alpha$alpha.drop)
rownames(dataf) <- rownames(var_alpha$alpha.drop)
return(dataf)
}
# "Italian in Australia"
ita_in_au <- do.call(rbind,lapply(sets,function(x) {
get_alpha(data=filtered_conv[filtered_conv$Context == "Italian in Australia",],
var=x)}))
ita_in_au$var <- sapply(strsplit(rownames(ita_in_au),split="\\."),function(x) x[1])
ita_in_au$var.full <- sapply(strsplit(rownames(ita_in_au),split="\\."),function(x) x[3])
ita_in_au$Context <- "Italian in Australia"
rownames(ita_in_au) <- NULL
# "German in Australia"
germ_in_au <- do.call(rbind,lapply(sets,function(x) {
get_alpha(data=filtered_conv[filtered_conv$Context == "German in Australia",],
var=x)}))
Some items were negatively correlated with the total scale and probably
should be reversed.
To do this, run the function again with the 'check.keys=TRUE' option
Some items ( knowledge.instru1 ) were negatively correlated with the total scale and
probably should be reversed.
To do this, run the function again with the 'check.keys=TRUE' option
germ_in_au$var <- sapply(strsplit(rownames(germ_in_au),split="\\."),function(x) x[1])
germ_in_au$var.full <- sapply(strsplit(rownames(germ_in_au),split="\\."),function(x) x[3])
germ_in_au$Context <- "German in Australia"
rownames(germ_in_au) <- NULL
# "English in Germany"
eng_in_germ <- do.call(rbind,lapply(sets[!(names(sets) %in% "comm.var")],function(x) {
get_alpha(data=filtered_conv[filtered_conv$Context == "English in Germany",],
var=x)}))
Some items were negatively correlated with the total scale and probably
should be reversed.
To do this, run the function again with the 'check.keys=TRUE' option
Some items ( people.ought1 ) were negatively correlated with the total scale and
probably should be reversed.
To do this, run the function again with the 'check.keys=TRUE' option
# the ones that makes issues
get_alpha(data=filtered_conv[filtered_conv$Context == "English in Germany",],
var=sets$ought.var)
Some items were negatively correlated with the total scale and probably
should be reversed.
To do this, run the function again with the 'check.keys=TRUE' option
Some items ( people.ought1 ) were negatively correlated with the total scale and
probably should be reversed.
To do this, run the function again with the 'check.keys=TRUE' option
eng_in_germ$var <- sapply(strsplit(rownames(eng_in_germ),split="\\."),function(x) x[1])
eng_in_germ$var.full <- sapply(strsplit(rownames(eng_in_germ),split="\\."),function(x) x[3])
eng_in_germ$Context <- "English in Germany"
rownames(eng_in_germ) <- NULL
# "English in Italy"
eng_in_ita <- do.call(rbind,lapply(sets[!(names(sets) %in% "comm.var")],function(x) {
get_alpha(data=filtered_conv[filtered_conv$Context == "English in Italy",],
var=x)}))
eng_in_ita$var <- sapply(strsplit(rownames(eng_in_ita),split="\\."),function(x) x[1])
eng_in_ita$var.full <- sapply(strsplit(rownames(eng_in_ita),split="\\."),function(x) x[3])
eng_in_ita$Context <- "English in Italy"
rownames(eng_in_ita) <- NULL
# combine
full_alpha <- rbind(eng_in_ita,eng_in_germ,germ_in_au,ita_in_au)
full_alpha %>% group_by(Context,var) %>%
summarise(st.alpha = unique(alpha.std.alpha),
G6=unique(alpha.G6.smc.)) %>%
ggplot(.,aes(x=var,y=st.alpha,colour=Context)) + geom_point() + geom_line(aes(group=Context)) + theme_bw()
all_melt <- all_melt %>% separate(variable,into=c("item","type"),sep="\\.",remove=FALSE)
Too few values at 646 locations: 9368, 9369, 9370, 9371, 9372, 9373, 9374, 9375, 9376, 9377, 9378, 9379, 9380, 9381, 9382, 9383, 9384, 9385, 9386, 9387, ...
p1=ggplot(all_melt,aes(x=variable,fill=value)) + geom_bar(position = "stack") +
facet_grid(Context~type,scales = "free") + ggtitle("Filtered dataset")+theme(axis.text.x = element_text(angle = 45, hjust = 1),axis.text=element_text(size=8))+theme_bw()
p2=ggplot(full_alpha,aes(x=var.full,y=drop.std.alpha,colour=Context)) + geom_point() + geom_line(aes(group=Context)) + theme_bw() + facet_wrap(~var,scales="free")
p4=ggplot(full_alpha,aes(x=var.full,y=drop.average_r,colour=Context)) + geom_point() + geom_line(aes(group=Context)) + theme_bw() + facet_wrap(~var,scales="free")
p3=full_alpha %>% group_by(Context,var) %>%
summarise(st.alpha = unique(alpha.std.alpha),
G6=unique(alpha.G6.smc.)) %>%
ggplot(.,aes(x=var,y=st.alpha,colour=Context)) + geom_point() + geom_line(aes(group=Context)) + theme(axis.text.x = element_text(angle = 45, hjust = 1),axis.text=element_text(size=8)) + theme_bw()
cowplot::plot_grid(p2,p3,nrow=2)
all <- read.csv(file.path("02-descriptive_data/merged_filtered_likertNumber.csv"))
dat <- all[,likert_variables1[!(likert_variables1 %in% c("necessity1","educated1"))]]
psych::alpha(dat,use="pairwise.complete.obs")
#detach("package:ggplot2", unload=TRUE)
fa_all <- function(data4,rot,minL,maxL,nfac=0,seed=5){
set.seed(seed)
# Save the orignal dataset
data_orig <- data4
# Define the rotations
orth <- c("varimax", "quartimax", "bentlerT", "equamax", "varimin", "geominT" , "bifactor" )
obl <- c("Promax", "promax", "oblimin", "simplimax", "bentlerQ", "geominQ", "biquartimin" ,"cluster")
alpha2 <- function(x){
alpha.1 <- function(C, R) {
n <- dim(C)[2]
alpha.raw <- (1 - tr(C)/sum(C)) * (n/(n - 1))
sumR <- sum(R)
alpha.std <- (1 - n/sumR) * (n/(n - 1))
smc.R <- smc(R)
G6 <- (1 - (n - sum(smc.R))/sumR)
av.r <- (sumR - n)/(n * (n - 1))
mod1 <- matrix(av.r, n, n)
Res1 <- R - mod1
GF1 = 1 - sum(Res1^2)/sum(R^2)
Rd <- R - diag(R)
diag(Res1) <- 0
GF1.off <- 1 - sum(Res1^2)/sum(Rd^2)
sn <- n * av.r/(1 - av.r)
Q = (2 * n^2/((n - 1)^2 * (sum(C)^3))) * (sum(C) * (tr(C %*%
C) + (tr(C))^2) - 2 * (tr(C) * sum(C %*% C)))
result <- list(raw = alpha.raw, std = alpha.std, G6 = G6,
av.r = av.r, sn = sn, Q = Q, GF1, GF1.off)
return(result)
}
if (!isCorrelation(x)) {
item.var <- apply(x, 2, sd, na.rm = T)
bad <- which((item.var <= 0) | is.na(item.var))
if ((length(bad) > 0) && delete) {
for (baddy in 1:length(bad)) {
warning("Item = ", colnames(x)[bad][baddy], " had no variance and was deleted")
}
x <- x[, -bad]
nvar <- nvar - length(bad)
}
response.freq <- response.frequencies(x, max = 10)
C <- cov(x, use = "pairwise")
}
else {
C <- x
}
R <- cov2cor(C)
alpha.total <- alpha.1(C, R)
return(alpha.total)
}
cicl<-0
par1<-1
par2<-1
par3<-1
while(par3>0){
# Selezione il numero di fattori consigliati
cat("Calculating the number of factors needed \n")
fact <- nfac
if(nfac==0){
fact<-fa.parallel(data4)$nfact
}
if(fact==1){
stop("Only one factor remains. Check the data or reduce the threshold")
}
#rot<-c("none", "varimax", "quartimax", "bentlerT", "geominT" , "bifactor", "promax", "oblimin", "simplimax", "bentlerQ", "geominQ" , "biquartimin" , "cluster" )
# Matrice uota che conterra le miedie degli alpha
range <- length(seq(minL,maxL,0.01))
mean.al<-matrix(0,range,length(rot))
# Sottociclo per il calcolo delle medie degli alpha (sui fattori) al variare sia della soglia sia della della rotazione
cat("Calculating the loadings thresholds \n")
for(b in 1:length(rot)){
cat("- Calculating the loadings thresholds for rotation",rot[b],"\n")
fa1<-fa(data4,fact,rotate=rot[b])
a<-fa1$loadings
class(a)<-"matrix"
colnames(a)<-paste("F",1:fact,sep="")
a<-as.data.frame(a)
a<-round(a,2)
a$D<-rownames(a)
ls1<-minL
m<-rep(0,range)
i<-1
nv<-fact
while(ls1<=maxL+0.01){
var<-lapply(1:nv,function(f)unique(a$D[abs(a[,f])>ls1]))
names(var)<-paste(colnames(a[,1:nv]))
# Do this if there are factors with length less than 2
if(any(as.numeric(summary(var)[,1])<2)){
# If a certain threshold and rotation gives only factors composed by 1 give it alpha=0
if(sum(as.numeric(summary(var)[,1])>1)==0){
m[i] <- 0
}else{
var1<-var[as.numeric(summary(var)[,1])>1]
al<-sapply(1:length(var1),function(v)alpha2(data4[,var1[[v]]])$std)
al<-data.frame(al)
#m[i]<-mean(t(al))
m[i]<-median(t(al))
}
}
# Do this if ALL the factors have length greater than 1
else{
al<-sapply(1:nv,function(v)alpha2(data4[,var[[v]]])$std)
al<-data.frame(al)
#m[i]<-mean(t(al))
m[i]<-median(t(al))
}
i<-i+1
ls1<-ls1+0.01
#cat(ls1)
}
mean.al[,b]<-m
#cat("\n")
}
# Creazione e stampa degli andamenti delle medie degli alpha
cat("Producing the threshold plot \n")
mean.al<-as.data.frame(mean.al)
colnames(mean.al)<-rot
mean.al$sl<-seq(minL,maxL,0.01)
mean.al1<-melt(mean.al,id.vars="sl")
zp<-ggplot(mean.al1,aes(x=sl,y=value,colour=variable))+geom_line()+labs(x="Soglia Loading",y="Alpha Medio",colour="Rotazione")
# Display the plot
print(zp)
max(mean.al)
#print(mean.al)
# Selezione della rotazione e della soglia che massimizzano le medie degli alpha
cat("Choosing the best rotation and threshold \n")
ind<-which(mean.al==max(mean.al),arr.ind=T)
if(class(ind)=="matrix"){
ind<-ind[1,]
}
rota<-names(mean.al)[ind[2]]
sogl<-mean.al$sl[ind[1]]
# Keep the same rotation that was selected in the first run
rot <- rota
# Calcolo fattoriale
fa1<-fa(data4,fact,rotate=rota)
a<-fa1$loadings
class(a)<-"matrix"
colnames(a)<-paste("F",1:fact,sep="")
a<-as.data.frame(a)
a<-round(a,2)
a$D<-rownames(a)
# Creazione dei fattori
var<-lapply(1:fact,function(f)unique(a$D[abs(a[,f])>sogl]))
names(var)<-paste(colnames(a[,1:fact]))
# Display the factors
cat("Displaying the factors \n")
print(var)
# togliamo le variabili che non entrano nei fattori al secondo ciclo
nc<- ncol(data4)
par1_names <- names(data4)[!(names(data4) %in% unlist(var))]
data4<-data4[,names(data4) %in% unlist(var)]
# Aggiorniamo il parametro di ciclo
par1<-nc-ncol(data4)
# togliamo le variabili che entrano in un fattore da sole (solo se non entrano in un altro fattore)
par4 <- 0
par4_names <- character()
len_fac <- cbind(1:fact,sapply(1:fact,function(v)length(var[[v]])))
if(any(len_fac[,2]==1)){
par4_nam<- data.frame(variable=as.character(unlist(var[ c(len_fac[len_fac[,2]==1,1]) ])))
par4_nam$variable <- as.character(par4_nam$variable)
par4_nam$ntimes= sapply(1:nrow(par4_nam), function(v)sum(unlist(var)%in%par4_nam[v,"variable"] ) )
# Do this only if the single variable enetrs in a variable alone
if(any(par4_nam$ntimes>1) & rot%in%obl ){
cat("- Will NOT remove variable(s)", par4_nam$variable[par4_nam$ntimes>1],"since they contribute to multiple factors","\n")
}
par4_names <- par4_nam$variable[par4_nam$ntimes==1]
par4 <- length(par4_names)
data4<-data4[,!names(data4) %in% par4_names]
}
# Togliamo le variabili che compaiono in piu fattori (se la rotazione e obliqua non effetuiamo tale operazione)
if(rota%in%obl){
par2<-0
s <- character()
}else{
s<-c(as.vector(unlist(var)))
s<-unique(s[duplicated(s)])
par2<-length(s)
data4<-data4[,!names(data4) %in% s]
}
cicl<-cicl+1
# Aggiorniamo il parametro di ciclo
par3<-par1+par2+par4
# Stampa diagnosi ciclo
cat(paste("Unused Variable:",par1),"\n")
cat("-",paste(par1_names),"\n")
cat(paste("Repeated variables:",par2),"\n")
cat("-",paste(s),"\n")
cat(paste("Single variables:",par4),"\n")
cat("-",paste(par4_names),"\n")
cat(paste("Rotation used:",rota),"\n")
cat(paste("Threshold chosen:",sogl),"\n")
cat(paste("End interation",cicl),"\n","\n")
}
cat(paste("Variables excluded in the process:"),names(data_orig)[!names(data_orig)%in%names(data4)],"\n")
return(data4)
}
#
likert_variables2 <- names(dat)
data1 <- dat[,likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]]
# Plot the correlations
corrplot(cor(data1,use = "pair"))
# check which variable does not correlated with any other vaiable
r1 <- cor(data1,use = "pair")
diag(r1) <- 0
r1 <- data.frame(r1)
temp <- data.frame(name=names(r1),cor=sapply(1:ncol(r1),function(v)any(r1[,v]>=0.3)) )
as.character(temp$name[temp$cor==F])
# Keep just the itemes with a r.cor greater or equal to 0.3
as <- psych::alpha(data1,check.keys=F)$item.stats
as$n1<-1:nrow(as)
summary(as$r.cor)
no_corr_macu <- rownames(as[abs(as$r.cor)<0.3,])
no_corr_macu
data1<-data1[,!names(data1)%in%no_corr_macu]
# check which items will decrease the alpha
al<- psych::alpha(data1)
drop<-al$alpha.drop
tot<-as.numeric(al$total$std.alpha)
drop <- drop[drop$std.alpha>tot,]
drop
# Check how much
drop$std.alpha-tot
# They don;t drop enough so leave it
#drop_alpha_macu <- rownames(drop)
#data1<-data1[,!names(data1)%in%drop_alpha_macu]
# check how many factors should be used
fap <- fa.parallel(data1)
fap
data_macu <- data1
## Perform the anlaysis for macular thickness
#Check again the out_maculiers
out_macu <- outlier(data_macu)
#abline(h=800)
#dat_imp_pheno[out_macu>800,1:20]
#out_maculiers_macu <- dat_imp_pheno$id1[out_macu>800]
#data_macu <- data_macu[out_macu<800,]
# Run the first set of analysisi and check what is cleaned out_macu!
rot=c("oblimin","promax")
# Take off the variables that give problem
#problem_var_macu <- c( "v07_spectralis" ,"v01_cyrrus" )
#data_macu <- data_macu[,!names(data_macu)%in%problem_var_macu]
library(ggplot2)
data_macu_facleaned <- fa_all(data_macu,rot,0.2,0.5)
# 0 Variable do not enter the factors
not_used_fact_macu <- names(data_macu)[!names(data_macu)%in%names(data_macu_facleaned)]
not_used_fact_macu
# Check again how many factors you need
#fa.parallel(data_macu_facleaned)
nfact_macu <- 6
# Run the actual factorial analysis on the final dataset
fa_macu<-fa(data_macu_facleaned,nfact_macu,rot="promax")
# Check whther any variable do not enter in any factor
lod <- fa_macu$loadings
class(lod) <- "matrix"
lod <- data.frame(lod)
lod$any <- apply(lod,1,function(v)any(abs(v)>=0.2) )
apply(lod[,1:nfact_macu],1,max )
lod[lod$any==F,]
# NONE, good!
# Plot the results
a<-fa_macu$loadings
class(a)<-"matrix"
colnames(a)<-paste("F",1:nfact_macu,sep="")
a<-as.data.frame(a)
a<-round(a,2)
a$D<-rownames(a)
a1 <- a
a1$D
a1 <- melt(a1,id.vars=c("D"))
a1$x <- runif(nrow(a1))
a1$inv <- ifelse(a1$value<0,"neg","pos")
a1$value[abs(a1$value)<0.2] <- 0
a1 <- a1[a1$value!=0,]
ggplot(a1)+geom_bar(aes(x=reorder(D, value) ,y=value),stat="identity")+facet_wrap(~variable,ncol = 2,scales = "free_y")+coord_flip()
#detach("package:ggplot2", unload=TRUE)
var<-lapply(1:nfact_macu,function(f)unique(a$D[abs(a[,f])>0.2]))
names(var)<-paste(colnames(a[,1:nfact_macu]))
al <- sapply(1:length(var),function(v) psych::alpha(data_macu_facleaned[,var[[v]]])$total$std.alpha)
al
# Alpha total
psych::alpha(data_macu_facleaned)$total$std.alpha
# they are ok...
psych::alpha(data_macu)$total$std.alpha
# Table of the factors
a$D <- NULL
a[abs(a)<0.2] <- 0
for(i in 1:ncol(a)){a[,i] <- as.character(a[,i])}
a[a=="0"] <- ""
loading_fact_macu <- a
loading_fact_macu
# Discriminant analysis: In this section i will test if the values of the variables kept in the dataframe by the factorial analysis are able to discriminate between subjects for which their total score lie in the 1st and 3rd quartile.
discr<-unique(data_macu_facleaned)
# Detrmine the total score
discr$punteggio<-rowSums(discr)
# Dividce the groups of people who lie in the 4rth and 1st quarile
hist(discr$punteggio)
quantile(discr$punteggio,na.rm = T)
discr1<-unique(discr[discr[,ncol(discr)]<=quantile(discr[,ncol(discr)],na.rm = T)[2],])
discr2<-unique(discr[discr[,ncol(discr)]>=quantile(discr[,ncol(discr)],na.rm = T)[4],])
# Wilcox Test the values of each single variable comparing the group of pople lien in the 1st and 3rd quartile
test<-data.frame(Item=colnames(discr[,1:(ncol(discr)-1)]),p.value=rep(0,(ncol(discr)-1)))
for(i in 1:(ncol(discr)-1)){
test[i,2]<-wilcox.test(discr1[,i],discr2[,i],alternative="two.sided")$p.value
}
test <- test[order(test$p.value),]
test
# they all discriminate!
# Calculate factors on the discarded variables
dat_disc <- dat[,no_corr_macu]
# check how many factors should be used
fap <- fa.parallel(dat_disc)
fap
library(ggplot2)
fa_disc <- fa(dat_disc,nfactors = 1,rotate = "oblimin")
# Plot the results
a<-fa_disc$loadings
class(a)<-"matrix"
colnames(a)<-paste("F",1,sep="")
a<-as.data.frame(a)
a<-round(a,2)
a$D<-rownames(a)
a1 <- a
a1$D
a1 <- melt(a1,id.vars=c("D"))
a1$x <- runif(nrow(a1))
a1$inv <- ifelse(a1$value<0,"neg","pos")
a1$value[abs(a1$value)<0.2] <- 0
a1 <- a1[a1$value!=0,]
library(ggplot2)
ggplot(a1)+geom_bar(aes(x=reorder(D, value) ,y=value),stat="identity")+facet_wrap(~variable,ncol = 2,scales = "free_y")+coord_flip()
#detach("package:ggplot2", unload=TRUE)
# Predict the factors
pred <- as.data.frame(predict(fa_macu,dat[,names(data_macu_facleaned)]))
names(pred) <- paste("Factor",1:nfact_macu,sep = "")
# Predict the factor from the discarded variables
pred_disc <- as.data.frame(predict(fa_disc,dat[,names(dat_disc)]))
names(pred_disc) <- paste("Factor",7,sep = "")
factors <- c(names(pred),names(pred_disc))
dat_complete <- cbind(dat,scale(pred),scale(pred_disc))
corrplot(cor(dat_complete[,likert_variables2],dat_complete[,factors],use = "pair"))
all_complete <- cbind(all,pred,pred_disc)
dat_plot <- melt(all_complete,id.vars = "Context",measure.vars = factors)
library(ggplot2)
ggplot(dat_plot)+geom_boxplot(aes(x=Context,y=value,color=Context))+facet_wrap(~variable)+coord_flip()+guides(color=F)
mod <- lm(Factor1~Context,data=all_complete)
summary(mod)
mod <- lm(Factor2~Context,data=all_complete)
summary(mod)
summary(lm(Factor4~Context,data=all_complete))
summary(lm(Factor6~Context,data=all_complete))
summary(lm(Factor7~Context,data=all_complete))
Seven, is the number of factors that would be present according to the study design. Using very relaxed cutoff of 0.2 to get rid of not important variables in each factor.
usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]
data1 <- dat[,usable_items]
fact <- 7
fa_basic <- fa(data1,fact)
fa_basic
# plot loadings
loadings_basic <- fa_basic$loadings
class(loadings_basic)<-"matrix"
colnames(loadings_basic)<-paste("F",1:7,sep="")
loadings_basic<-as.data.frame(loadings_basic)
loadings_basic<-round(loadings_basic,2)
loadings_basic$D<-rownames(loadings_basic)
a1 <- loadings_basic
a1 <- melt(a1,id.vars=c("D"))
a1$x <- runif(nrow(a1))
a1$inv <- ifelse(a1$value<0,"neg","pos")
a1$value[abs(a1$value)<0.2] <- 0
a1 <- a1[a1$value!=0,]
a1 <- a1 %>% separate(D,into = c("Variable","Item"),remove=FALSE,sep="[.]")
ggplot(a1)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+facet_wrap(~variable,ncol = 2,scales = "free_y")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")
# Table of the factors
loadings_basic$D <- NULL
loadings_basic[abs(loadings_basic)<0.2] <- 0
for(i in 1:ncol(loadings_basic)){loadings_basic[,i] <- as.character(loadings_basic[,i])}
loadings_basic[loadings_basic=="0"] <- ""
loading_fact_reduced <- loadings_basic
loading_fact_reduced
# predict values per samples
pred_basic <- as.data.frame(predict(fa_basic,data1))
names(pred_basic) <- paste("Factor",1:fact,sep = "")
factors <- names(pred_basic)
dat_complete_basic <- cbind(dat,scale(pred_basic))
corrplot(cor(dat_complete_basic[,usable_items],dat_complete_basic[,factors],use = "pair"))
all_complete_basic <- cbind(all,pred_basic)
dat_plot_basic <- melt(all_complete_basic,id.vars = "Context",measure.vars = factors)
library(ggplot2)
ggplot(dat_plot_basic)+geom_boxplot(aes(x=Context,y=value,color=Context))+facet_wrap(~variable)+coord_flip()+guides(color=F)
Using very relaxed cutoff of 0.2 to get rid of not important variables in each factor.
usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]
data1 <- dat[,usable_items]
# From a statisticak point of view
fap <- fa.parallel(data1)
fact <- 6
fa_basic <- fa(data1,fact)
fa_basic
# plot loadings
loadings_basic <- fa_basic$loadings
class(loadings_basic)<-"matrix"
colnames(loadings_basic)<-paste("F",1:6,sep="")
loadings_basic<-as.data.frame(loadings_basic)
loadings_basic<-round(loadings_basic,2)
loadings_basic$D<-rownames(loadings_basic)
a1 <- loadings_basic
a1 <- melt(a1,id.vars=c("D"))
a1$x <- runif(nrow(a1))
a1$inv <- ifelse(a1$value<0,"neg","pos")
a1$value[abs(a1$value)<0.2] <- 0
a1 <- a1[a1$value!=0,]
a1 <- a1 %>% separate(D,into = c("Variable","Item"),remove=FALSE,sep="[.]")
ggplot(a1)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+facet_wrap(~variable,ncol = 2,scales = "free_y")+coord_flip()+ geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")
# Table of the factors
loadings_basic$D <- NULL
loadings_basic[abs(loadings_basic)<0.2] <- 0
for(i in 1:ncol(loadings_basic)){loadings_basic[,i] <- as.character(loadings_basic[,i])}
loadings_basic[loadings_basic == "0"] <- ""
loading_fact_reduced <- loadings_basic
loading_fact_reduced
# predict values per samples
pred_basic <- as.data.frame(predict(fa_basic,data1))
names(pred_basic) <- paste("Factor",1:fact,sep = "")
factors <- names(pred_basic)
dat_complete_basic <- cbind(dat,scale(pred_basic))
corrplot(cor(dat_complete_basic[,usable_items],dat_complete_basic[,factors],use = "pair"))
all_complete_basic <- cbind(all,pred_basic)
dat_plot_basic <- melt(all_complete_basic,id.vars = "Context",measure.vars = factors)
library(ggplot2)
ggplot(dat_plot_basic) + geom_boxplot(aes(x=Context,y=value,color=Context))+facet_wrap(~variable)+coord_flip()+guides(color=F)
# error bar
sum_stat <- dat_plot_basic %>% group_by(Context,variable) %>%
summarise(meanFac = mean(value,na.rm=TRUE),
stdFac = sd(value,na.rm=TRUE),
nObs = length(Context[!is.na(value)])) %>%
mutate(stdMean = stdFac/sqrt(nObs))
ggplot(sum_stat,aes(x=Context,y=meanFac,colour=Context)) +
geom_errorbar(aes(ymin=meanFac-stdMean, ymax=meanFac+stdMean)) + facet_wrap(~variable,scales="free_y") + geom_point() +theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplot(sum_stat,aes(x=variable,y=meanFac,colour=variable)) +
geom_errorbar(aes(ymin=meanFac-stdMean, ymax=meanFac+stdMean)) + facet_wrap(~Context,scales="free_y") + geom_point()
kable(sum_stat)
usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]
#
dat <- all[,c(usable_items,"Context")]
dat_noNA <- dat[rowSums(is.na(dat)) == 0,]
all_noNA <- all[rowSums(is.na(dat)) == 0,]
# na to remove
table(rowSums(is.na(dat)),dat$Context)
get_residuals <- function(item,pred = dat$Context){
mod <- lm(item ~ pred)
return(mod$residuals)
}
dat_onlyItems <- dat_noNA[,usable_items]
#data1 <- dat[,usable_items]
# dat_onlyItems <- data1
applygetRes <- apply(as.matrix(dat_onlyItems),2,get_residuals,pred=dat_noNA$Context)
# Factanal
# From a statisticak point of view
fap <- fa.parallel(applygetRes)
fact <- 6
fa_basic <- fa(applygetRes,fact)
fa_basic
# plot loadings
loadings_basic <- fa_basic$loadings
class(loadings_basic)<-"matrix"
colnames(loadings_basic)<-paste("F",1:6,sep="")
loadings_basic<-as.data.frame(loadings_basic)
loadings_basic<-round(loadings_basic,2)
loadings_basic$D<-rownames(loadings_basic)
a1 <- loadings_basic
a1 <- melt(a1,id.vars=c("D"))
a1$x <- runif(nrow(a1))
a1$inv <- ifelse(a1$value<0,"neg","pos")
a1$value[abs(a1$value)<0.2] <- 0
a1 <- a1[a1$value!=0,]
a1 <- a1 %>% separate(D,into = c("Variable","Item"),remove=FALSE,sep="[.]")
ggplot(a1)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+facet_wrap(~variable,ncol = 2,scales = "free_y")+coord_flip()+ geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")
# Table of the factors
loadings_basic$D <- NULL
loadings_basic[abs(loadings_basic)<0.2] <- 0
for(i in 1:ncol(loadings_basic)){loadings_basic[,i] <- as.character(loadings_basic[,i])}
loadings_basic[loadings_basic == "0"] <- ""
loading_fact_reduced <- loadings_basic
loading_fact_reduced
# predict values per samples
pred_basic <- as.data.frame(predict(fa_basic,dat_onlyItems))
names(pred_basic) <- paste("Factor",1:fact,sep = "")
factors <- names(pred_basic)
dat_complete_basic <- cbind(dat_onlyItems,scale(pred_basic))
corrplot(cor(dat_complete_basic[,usable_items],dat_complete_basic[,factors],use = "pair"))
all_complete_basic <- cbind(all_noNA,pred_basic)
dat_plot_basic <- melt(all_complete_basic,id.vars = "Context",measure.vars = factors)
library(ggplot2)
ggplot(dat_plot_basic) + geom_boxplot(aes(x=Context,y=value,color=Context))+facet_wrap(~variable)+coord_flip()+guides(color=F)
# error bar
sum_stat <- dat_plot_basic %>% group_by(Context,variable) %>%
summarise(meanFac = mean(value,na.rm=TRUE),
stdFac = sd(value,na.rm=TRUE),
nObs = length(Context[!is.na(value)])) %>%
mutate(stdMean = stdFac/sqrt(nObs))
ggplot(sum_stat,aes(x=Context,y=meanFac,colour=Context)) +
geom_errorbar(aes(ymin=meanFac-stdMean, ymax=meanFac+stdMean)) + facet_wrap(~variable,scales="free_y") + geom_point() +theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplot(sum_stat,aes(x=variable,y=meanFac,colour=variable)) +
geom_errorbar(aes(ymin=meanFac-stdMean, ymax=meanFac+stdMean)) + facet_wrap(~Context,scales="free_y") + geom_point()
kable(sum_stat)
## linear model
demographics_var <- c("Age","Gender","L1","speak.other.L2","study.other.L2","origins","year.studyL2","other5.other.ways","degree","roleL2.degree","study.year","prof","L2.VCE","uni1.year","Context")
usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]
data1 <- all[all$Context %in% "English in Germany",usable_items]
# From a statisticak point of view
fap <- fa.parallel(data1)
fact <- 6
fa_basic <- fa(data1,fact)
fa_basic
# plot loadings
loadings_basic <- fa_basic$loadings
class(loadings_basic)<-"matrix"
colnames(loadings_basic)<-paste("F",1:6,sep="")
loadings_basic<-as.data.frame(loadings_basic)
loadings_basic<-round(loadings_basic,2)
loadings_basic$D<-rownames(loadings_basic)
a1 <- loadings_basic
a1 <- melt(a1,id.vars=c("D"))
a1$x <- runif(nrow(a1))
a1$inv <- ifelse(a1$value<0,"neg","pos")
a1$value[abs(a1$value)<0.2] <- 0
a1 <- a1[a1$value!=0,]
a1 <- a1 %>% separate(D,into = c("Variable","Item"),remove=FALSE,sep="[.]")
ggplot(a1)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+facet_wrap(~variable,ncol = 2,scales = "free_y")+coord_flip()+ geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")
# Table of the factors
loadings_basic$D <- NULL
loadings_basic[abs(loadings_basic)<0.2] <- 0
for(i in 1:ncol(loadings_basic)){loadings_basic[,i] <- as.character(loadings_basic[,i])}
loadings_basic[loadings_basic=="0"] <- ""
loading_fact_reduced <- loadings_basic
loading_fact_reduced
# predict values per samples
pred_basic <- as.data.frame(predict(fa_basic,data1))
names(pred_basic) <- paste("Factor",1:fact,sep = "")
factors <- names(pred_basic)
dat_complete_basic <- cbind(dat,scale(pred_basic))
corrplot(cor(dat_complete_basic[,usable_items],dat_complete_basic[,factors],use = "pair"))
all_complete_basic <- cbind(all,pred_basic)
dat_plot_basic <- melt(all_complete_basic,id.vars = "Context",measure.vars = factors)
library(ggplot2)
ggplot(dat_plot_basic)+geom_boxplot(aes(x=Context,y=value,color=Context))+facet_wrap(~variable)+coord_flip()+guides(color=F)